Using mobile data to improve weather information

ABSTRACT

A system for using mobile data to improve weather information is provided. The system includes a weather prediction station configured to receive stationary observation data provided by a plurality of stationary weather stations along with data from a plurality of input weather models and generate unified weather model estimates based on the stationary observation data, the input weather model data, and a processor. The processor is configured to aggregate mobile observation data provided by a plurality of non-stationary sensors and use the aggregated mobile observation data to adjust the weather model estimates.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims priority to U.S.application Ser. No. 16/864,377, filed May 1, 2020, the disclosure ofwhich is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The embodiments described below relate to weather information and, moreparticularly, to using mobile data to improve the weather information.

BACKGROUND

There is growing interest in using mobile observations to improve thecurrent conditions estimates and (future) forecasts produced by weathermodels. Mobile observations are attractive because they are available atlarge numbers of locations, such as when provided by mobile phones orvehicles. Yet these observations have several critical disadvantages,compared to observations from stationary sensors (e.g., weatherstations), requiring novel methods to make them useful for improvingweather model information.

For example, single observations, often from consumer-quality devices orsensors primarily intended for other purposes, tend to have low quality.Indeed, the quality is often significantly worse than what can beobtained from the weather models themselves. That makes it difficult touse individual mobile observations alone to improve the weather models.Also, by definition, mobile observations are made at locations thatchange regularly. In contrast, important weather modeling processesrequire a time series of data from a location to enable statisticalprocesses. Additionally, in some cases, observations may be relevant butmay not exactly match the information needed for, or produced by,weather models. For example, vehicle traction control system data maynot relate unambiguously to whether a condition such as ice is presentor not on a road; low traction values may correspond to ice but couldalso reflect other causes of low traction.

As a specific relevant example of this need for mobile observations,their use is of particular interest within the transportation-relatedsector, such as for providing accurate along-road weather-relatedinformation. This use includes both atmospheric weather conditions, suchas temperature and wind, as well as “road weather”, such as road surfacetemperature and weather-related conditions (e.g., dry, moist, wet, snow,ice—including amount).

Historically, observations of these parameters have been made at RoadWeather Information System (RWIS) sites, located along roads andgenerally operated by governmental transportation agencies. As they areexpensive to acquire and maintain, the number of existing RWIS isrelatively small; they only sparsely sample the road network.

Accordingly, there is a need to use mobile data, particularly mobiledata provided by plentiful and readily available mobile sensors, toimprove weather information. There is also a need to improve the qualityand suitability of the mobile data provided by the mobile sensors foruse in improving the weather information.

SUMMARY

A system for using mobile data to improve weather information isprovided. According to an embodiment, the system comprises a weatherprediction station configured to receive stationary observation dataprovided by a plurality of stationary weather stations along with datafrom a plurality of input weather models and generate unified weathermodel estimates based on the stationary observation data, and the inputweather model data. The system also comprises a processor configured toaggregate mobile observation data provided by a plurality ofnon-stationary sensors and use the aggregated mobile observation data toadjust the unified weather model estimates.

A method for using mobile data to improve weather information isprovided. According to an embodiment, the method comprises receivingstationary observation data provided by a plurality of stationaryweather stations, receiving data from a plurality of input weathermodels, generating unified weather model estimates based on thestationary observation data and the input weather model data,aggregating mobile observation data provided by a plurality ofnon-stationary sensors, and using the aggregated mobile observation datato adjust the unified weather model estimates.

According to an aspect, a system (100) for using mobile data to improveweather information comprises a weather prediction station (120)configured to receive stationary observation data provided by aplurality of stationary weather stations (110), receive data from aplurality of input weather models (115), and generate unified weathermodel estimates based on the stationary observation data, the inputweather model data, and a processor (130). The processor (130) isconfigured to aggregate mobile observation data provided by a pluralityof non-stationary sensors (140) and use the aggregated mobileobservation data to adjust the unified weather model estimates.

Preferably, the processor (130) is further configured to determinevirtual observation data based on the adjustment of the unified weathermodel estimates, the virtual observation data being an estimate ofobservation data that would have been provided by a plurality ofnon-existent stationary weather stations.

Preferably, the processor (130) being configured to determine virtualobservation data based on the adjustment of the unified weather modelestimates comprises the processor (130) being configured to determineclimatological values based on the aggregated mobile observation dataand use the climatological values to determine the virtual observationdata.

Preferably, the processor (130) is further configured to use the virtualobservation data to perform post-processing on a weather predictionmodel.

Preferably, the processor (130) being configured to use the aggregatedmobile observation data to adjust the unified weather model estimatescomprises the processor (130) being configured to spatially correlatethe mobile observation data to a virtual observation location (230) andadjusting the weather model estimates at the virtual observationlocation (230).

Preferably, the processor (130) being configured to spatially correlatethe aggregated mobile observation data to a virtual observation location(130) comprises the processor (130) being configured to determine aspatial correlation distance (dbinCORR) defining a distance over whichan observation may be usefully extrapolated to adjust the weather modelestimates.

Preferably, the mobile observation data provided by the plurality ofnon-stationary sensors (130) is organized by spatiotemporal bin (210)defined by location and time parameters.

Preferably, the system (100) further comprises a plurality of stationaryweather stations (110) configured to provide the stationary observationdata of weather conditions at the locations of the stationary weatherstations (110).

According to an aspect, a method for using mobile data to improveweather information comprises receiving stationary observation dataprovided by a plurality of stationary weather stations, receiving datafrom a plurality of input weather models, generating unified weathermodel estimates based on the stationary observation data and the inputweather model data, aggregating mobile observation data provided by aplurality of non-stationary sensors, and using the aggregated mobileobservation data to adjust the unified weather model estimates.

Preferably, the method further comprises determining virtual observationdata based on the adjustment of the unified weather model estimates, thevirtual observation data being an estimate of observation data thatwould have been provided by a plurality of non-existent stationaryweather stations.

Preferably, determining virtual observation data based on the adjustmentof the unified weather model estimates comprises determiningclimatological values based on the aggregated mobile observation dataand using the climatological values to determine the virtual observationdata.

Preferably, the method further comprises using the virtual observationdata to perform post-processing on a weather prediction model.

Preferably, using the aggregated mobile observation data to adjust theunified weather model estimates comprises spatially correlating themobile observation data to a virtual observation location and adjustingthe unified weather model estimates at the virtual observation location.

Preferably, spatially correlating the mobile observation data to avirtual observation location comprises determining a spatial correlationdistance defining a distance over which an observation may be usefullyextrapolated to adjust the unified weather model estimates.

Preferably, the mobile observation data provided by the plurality ofnon-stationary sensors is organized by spatiotemporal bin defined bylocation and time parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

The same reference number represents the same element on all drawings.It should be understood that the drawings are not necessarily to scale.

FIG. 1 shows a system 100 for using mobile data to improve weatherinformation.

FIG. 2 shows another view of the system 100 depicted in FIG. 1 .

FIG. 3 shows a method 300 for using mobile data to improve weatherinformation.

DETAILED DESCRIPTION

FIGS. 1-3 and the following description depict specific examples toteach those skilled in the art how to make and use the best mode ofembodiments of using mobile data to improve weather information. For thepurpose of teaching inventive principles, some conventional aspects havebeen simplified or omitted. Those skilled in the art will appreciatevariations from these examples that fall within the scope of the presentdescription. Those skilled in the art will appreciate that the featuresdescribed below can be combined in various ways to form multiplevariations of using mobile data to improve weather information. As aresult, the embodiments described below are not limited to the specificexamples described below, but only by the claims and their equivalents.

FIG. 1 shows a system 100 for using mobile data to improve weatherinformation. As shown in FIG. 1 , the system includes a stationaryweather station 110, an input weather model 115, and a weatherprediction station 120. As shown in FIG. 1 , the stationary weatherstation 110 and the weather prediction station 120 are communicativelycoupled with each other. The weather prediction station 120 may beconfigured to receive stationary observation data provided by thestationary weather station 110. Additionally, or alternatively, theweather prediction station 120 may be configured to receive stationaryobservation data provided by one or more other stationary weatherstations and model data from one or more other input weather models.

As shown in FIG. 1 , the system 100 also includes a processor 130 thatis communicatively coupled with the weather prediction station 120. Asshown in FIG. 1 , the processor 130 is separate from the weatherprediction station 120, although the processor 130 may be part of theweather prediction station 120. Although the system 100 is shown asincluding the stationary weather station 110, the system 100 may becomprised of the weather prediction station 120 and the processor 130.The processor 130 may be configured to perform post-processing on aweather prediction model. With more specificity, the processor 130 maybe configured to use virtual observations data of locations withoutstationary weather stations. The virtual observation data may beestimates of observation data that would have been provided by anon-existent stationary weather station. The virtual observation datamay be based on mobile observation data, as is described in more detailin the following.

The system 100 may also include a non-stationary sensor 140 that iscommunicatively coupled with the processor 130. Although not shown, thenon-stationary sensor 140 may be alternatively or additionallycommunicatively coupled to the weather prediction station 120 so as toreceive or send data. For example, where the processor 130 is part ofthe weather prediction station 120, the non-stationary sensor 140 may becommunicatively coupled with the weather prediction station 120. Thedata provided by the non-stationary sensor 140 may be mobile observationdata.

The stationary weather station 110 may be configured to determineweather conditions at or proximate to the stationary weather station110. Accordingly, the stationary weather station 110 may be configuredto provide stationary observation data. The stationary observation datamay include a plurality of stationary observation values that can beused by the weather prediction station.

The input weather model 115 is shown as being communicatively coupled tothe weather prediction station 120. The input weather model 115 mayinclude a weather model of an area that includes the system 100, such asa portion of system 100. A plurality of the input weather models 115 mayprovide data to the weather prediction station 120.

The weather prediction station 120 may use the stationary observationdata to determine unified weather model estimates by generating datausing one or more weather prediction model methods. That is, the unifiedweather model estimates may be based on the stationary observation data.The unified weather model estimates may include current conditionestimates (e.g., for locations other than the stationary weatherstation) and/or (future) forecasts. The weather prediction station 120may generate the unified weather model estimates based on one or moreweather prediction models using any suitable method, as the followingexplains.

The weather prediction station 120 may assimilate the stationaryobservation data into Numerical Weather Prediction (NWP) models. Acharacteristic of this approach is that NWP models typically require anhour or more for computation, once observations are received, before theforecast is available. The “latency” may be undesirable when the desiredinformation concerns using observations to improve estimates of currentconditions or near-term (first hour or so) forecasts. To compensate forthe latency and improve accuracy, among other things, the NWP model maybe adjusted with additional “post-processing” steps using more currentstationary observation data. The processor 130 may perform suchpost-processing. The accumulation of these steps, involving multipleinput models and observational data, combined in ways that improve oneach input, is referred to as the “unified” weather model.

For example, an NWP estimate of air temperature determined by theweather prediction model, computed using observations that are an hourold or more, may be different from the current observed temperature by4° C. for any of several reasons. Post-processing performed by theprocessor 130 can adjust this model estimate to match the observed valueusing a compute process that takes minutes. Moreover, thatobservation-improved current condition estimate (and related near-termforecasts) of the stationary weather stations can be used to improvesimilar estimates/forecasts at locations in a region surrounding theobservation location. Furthermore, availability of observations atlocations having no stationary sensors can improve weather modelinformation overall.

An input to post-processing performed by the processor 130 may consistof multiple NWP models along with observations. Post-processing canimprove on the accuracy of the input NWP models, at the observationlocations, in any of three or more steps, including but not limited to:a) statistical correction of individual NWP models through biasadjustment, b) guidance for consensus combinations of those models togenerate an improved estimate/forecast, and c) ensuring the finalestimate/forecast matches the observation for the current time at theobservation location. A fourth step, added to extend post-processing tonon-observation locations, involves adjusting model information usingguidance from nearby observations by stationary observation locations.The stationary observation data may be used in each of the above foursteps.

The stationary observation data produce an observation time-series at asingle location. Accordingly, the observation time-series can bestatistically analyzed as part of the post-processing for that location.In contrast, the mobile observation data for an individual mobile sensor140 do not produce such a time-series for a fixed location, precludingsuch statistical analysis on the mobile observation data. To allow forpost processing at a fixed location using the mobile observation data,the processor 130 may aggregate the mobile observation data. Suchaggregation may involve grouping observations from multiple mobilesensors 140, organized into spatiotemporal bins associated with limitedspatial distances and time periods. Moreover, the aggregated mobileobservation data may be interpolated into virtual observation data. Thevirtual observation data may be for locations other than the location ofthe mobile observation data or the stationary weather stations, as willbe described in more detail in the following with reference FIG. 2 . Thevirtual observation data can be used as an input to all of the NWPgeneration and post-processing steps.

FIG. 2 shows another view of the system 100 depicted in FIG. 1 . Asshown in FIG. 2 , the system 100 includes a plurality of the stationaryweather stations 110 and input weather models 115 described above. Thestationary weather stations 110 are represented as small circles and theinput weather models 115 are depicted as small squares. The system 100also includes the weather prediction station 120 and the processor 130described above. The weather prediction station 120 and the processor130 are communicatively coupled with each other. Only two of theplurality of the stationary weather stations 110 and only one of theinput weather models 115 are shown as being communicatively coupled withthe weather prediction station 120, although all of the stationaryweather stations 110 and input weather models 115 may be communicativelycoupled with the weather prediction station 120. A plurality of thenon-stationary sensors 140 are also shown. The plurality of thenon-stationary sensors 140 are shown as vehicles, or a part of thevehicles, although any suitable non-stationary sensor may be employed.The plurality of the non-stationary sensors 140 are shown as being on atwo-lane road with arrows illustrating motion directions of thenon-stationary sensors 140.

As shown in FIG. 2 , the system 100 also includes spatiotemporal bins210 that are shown as including a portion of the two-lane road shown inFIG. 2 . As can be appreciated, when a plurality of the non-stationarysensors 140 traverse one of the spatiotemporal bins 210 during adetermined time period, the mobile observation data provided by each ofthose non-stationary sensors 140 are in the same spatiotemporal bin 210and therefore are representative of conditions, such as weatherconditions, at a location and time of a given spatiotemporal bin 210.Spatially and temporally coincident mobile observation data fromdifferent non-stationary sensors 140 may be aggregated in acorresponding spatiotemporal bin 210.

Also shown is a spatial correlation region 220 that corresponds to oneof the spatiotemporal bin 210. The spatial correlation region 220 is anarea where an observation of the aggregated mobile observation data inthe corresponding spatiotemporal bin 210 may be usefully extrapolated toadjust a weather prediction model at a given location. Suchextrapolations may be the virtual observations discussed above and maybe located at corresponding virtual observation locations 230 shown inFIG. 2 , illustrated as boxes having an ‘X’. The spatial correlationregion 220 is defined by a spatial correlation distance dbinCORR for thecorresponding spatiotemporal bin 210, as will be described in moredetail in the following.

It should be noted that somewhat distinct branches of the method may beneeded for continuous variables and discrete variables. Continuousvariables are those whose value tends to vary smoothly in space and timeover a continuous range. Examples include temperature, dew point, windspeed, liquid/frozen depth on road, and similar variables. Discretevariables have discrete levels. The discrete variables changediscontinuously in space and/or time. Examples include road state (e.g.,dry, moist, wet, snow, ice), precipitation occurrence, precipitationtype, wiper status, and similar variables.

The methodology for generating virtual observations from mobileobservations can include four steps: quality control and aggregation,compute climatology, compute spatial correlation, and compute virtualobservations. Quality control and aggregation may be used because mobileobservations are typically of lower quality than industry-standardstationary observations (such as those from weather stations atairports). Thus, new quality control processes may need to be applied tothe mobile observation data. One means for reducing errors fromindividual mobile devices is to aggregate multiple observations into thespatiotemporal bins 210. For example, all vehicles located within a 1-kmroad segment during the course of an hour could be aggregated into aspatiotemporal bin with spatial size of 1-km and temporal size of 1hour. This “aggregated observation” may include multiple statisticalparameters, such as a mean and a variance. The number of samples in thespatiotemporal bin is related to the accuracy of this aggregatedobservation, so higher quality is likely at locations where more mobileobservations are available. Techniques for inter-comparison of mobiledevices having sensors (i.e., non-stationary sensors), such as when theyare coincident in space-time, may also be important.

As to climatology, if the “aggregated observations” for a spatiotemporalbin are stored and accumulated over time, it is possible to computeaggregated observation climatological values. For example, aggregatedobservations of a variable for every hour over a one-month period couldbe averaged over that month to determine the mean value of the variableduring that month at each bin location, perhaps as a function oftime-of-day or other parameter to properly reflect expectedclimatological variability. The climatology may be computed for staticperiods, such as monthly, or for dynamic periods, such as trailing30-days.

With respect to computing spatial correlations, drawing from the dataused to compute the climatology, it is also possible to use the binnedaggregated observations to compute the spatial correlation distancedbinCORR for each variable and each spatiotemporal bin 210. As mentionedabove, the spatial correlation distance dbinCORR tells us the distanceover which an observation may be usefully extrapolated to adjustprediction models at nearby locations. In some cases, it may be usefulto compute this on a contingent basis, such as the correlation distanceof road condition if snow is the predicted or observed road condition.The spatial correlation distance dbinCORR could also be computed as afunction of spatial direction, such as a different value for each of thetwo travel directions along a road. Accordingly, the spatial correlationdistance dbinCORR may be viewed as a vector originating from a locationcorresponding to a given spatiotemporal bin 210. Accordingly, althoughthe spatial correlation region 220 is shown as a circle, other shapesmay be employed. As with the climatology, the spatial correlation may becomputed for static periods, such as monthly, or for dynamic periods,such as trailing 30-days, and may be a function of various parameterssuch as time of day.

With respect to computing virtual observations, the spatial correlationdistance dbinCORR enables computation of a generally non-sparse set ofobservation-equivalents, which will be called “virtual observations”,from potentially sparse real-time aggregated mobile observations.Virtual observations can then be input to NWP generation and to allstages of traditional post-processing: fine-scale model bias adjustment,location-specific consensus combination, real-time error correction, andextrapolation of observations in real-time. To understand the processfor generating the virtual observations, suppose that for a givendate/time we have an aggregated observation YA(AggObs) for variable Y atlocation A but no observation at location B, such as the virtualobservation locations 230 shown in FIG. 2 . The aggregated observationYA(AggObs) can be used to improve the weather information at location A,but we would also like to use it to improve the weather information atlocation B (it is expected that B may have one or more nearby locationsA each with a valid aggregated observation, or may have no suchlocations A). To frame the solution, assume that the unified modelestimates (see following description) are YA(UniModel) and YB(UniModel),the separation between sites A and B is dA,B, and the computed spatialcorrelation distance at location A is dACORR.

For continuous variables, virtual observations Y^((Virtual)) atlocations A and B can be computed from the unified model estimates forlocations A and B and the aggregated mobile observation at location A,using the following equations:

Y _(n) ^((Virtual)) =Y _(n) ^((UniModel)) −Y _(n) ^((ObsAdJ)) for n=A,B

where:

Y_(n) ^((ObsAdj)) is an observation-based adjustment.

The observation-based adjustment Y_(n) ^((ObsAdj)) may be defined as,for example:

Y _(n) ^((ObsAdj)) =Q _(A) K·D _(A,B)·(Y _(A) ^((UniModel)) −Y_(A(AggObs)));

where:

-   -   K is a local or global user-defined sensitivity factor between 0        and 1, set to manage the desired overall sensitivity of the        system to the mobile observations adjustment process;    -   Q_(n) is a user-defined, location-based quality factor between 0        and 1; and    -   D_(A,B) is a factor based on the correlation distance, to reduce        the applied adjustment as location separation increases.        The location-based quality factor Q_(n) may be defined as, for        example:

$\begin{matrix}{Q_{n} = {H_{n} \cdot \left( {s_{0}/s_{n}} \right)}} & {{{for}s_{n}} > s_{0}} \\{= H_{n}} & {s_{n}<=s_{0;}}\end{matrix}$

where:

-   -   n is a location index (e.g., ‘A’ or ‘B’);    -   s_(n) is a standard deviation reflecting uncertainty in the        observation/climatology for variable Y at location n;    -   s₀ is a user-defined threshold uncertainty reflecting the        maximum acceptable un-corrected error in the observations and        climatology for variable Y for use in computing virtual        observations; and    -   H_(n) is an observation ambiguity factor, nominally equal to 1        for many variables (discussed later in the document).        The correlation factor D_(A,B) may be defined as, for example:

$\begin{matrix}{D_{A,B} = {1 - \left( {d_{A,B}/\left( {m \cdot d_{A}^{CORR}} \right)} \right)}} & {{{for}d_{A,B}}<={m \cdot d_{A}^{CORR}}} \\{= 0} & {{{for}d_{A,B}} > {m \cdot {d_{A}^{CORR}.}}}\end{matrix}$

where:

-   -   m is a user-set sensitivity factor, defining how many        correlation distances should be adjusted.

There are three important cases of the correlation factor D_(A,B).First, at location A, B=A so a distance may be computed asd_(A,B)->d_(A,A)=0 and thereby the resulting correlation factorD_(A,B)=1. Second, for where the distance d_(A,B) equals m·d_(A) ^(CORR)or less, the correlation factor D_(A,B) will be 0<D_(A,B)<1. In thiscase, a distance-weighted A-observation-based adjustment will be made atlocation B. Third, for larger distances d_(A,B), the spatial correlationfactor D_(A,B)=0. In this case, no A-observation-based adjustment willbe made at location B. In other words, the virtual observations at A andB are what one would expect.

That is, when the quality of the mobile observations is sufficientlyhigh (i.e., Q_(A) and K equal to 1), the virtual observation Y_(A)^((Virtual)) at location A simply takes on the value of the aggregatedobservation at A. As quality decreases, the virtual observation Y_(A)^((virtual)) at location A trends toward the unified model value.Similarly, the virtual observation Y_(B) ^((virtual)) at location Bequals the unified model value adjusted by a portion of the aggregatedobservation at A, that portion determined by the quality of theaggregated observation at A and the distance of B from A as compared tothe computed correlation distance. If no adjustment is made, the virtualobservation assumes the value of the unified model at B.

Updated estimates of quality, such as standard deviation, can becomputed for each virtual observation derived from the underlyingaggregated observation values. For locations impacted by multipleobservations, the observation adjustment Y_(n) ^((ObsAdj)) values can beaccumulated from n neighboring observation locations A_(n), eachseparated from B by less than some multiple m of the correlationdistance, and added in a normalized manner such as bydistance-weighting.

For discrete values, the virtual observations Y^((Virtual)) at locationsA and B can be computed from the unified model estimates for locations Aand B and the aggregated mobile observation at location A, using thefollowing equations:

$\begin{matrix}{Y_{A}^{({Virtual})} = Y_{A}^{({AggObs})}} & {{{for}Q_{A}}>=Q^{thresh}} \\{= Y_{A}^{({UniModel})}} & {{{for}Q_{A}} < Q^{thresh}}\end{matrix}$ $\begin{matrix}{Y_{B}^{({Virtual})} = Y_{A}^{({AggObs})}} & {{{for}Q_{A}}>={Q^{thresh}{and}d_{A,B}}<={Q_{A}{K\left( {m \cdot d_{A}^{CORR}} \right)}}} \\{= Y_{B}^{({UniModel})}} & {{{{for}Q_{A}} < Q^{thresh}},} \\\text{} & {{{for}Q_{A}}>={Q^{thesh}{and}d_{A,B}} > {Q_{A}{K\left( {m \cdot d_{A}^{CORR}} \right)}}}\end{matrix}$

where:

K, Q^(A), and m are as defined above for continuous variables; and

Q^(thresh) is a user-defined quality threshold for modifying Y.

In other words, the discrete unified model Y_(A) ^((UniModel)) and Y_(B)^((UniModel)) values are replaced by the discrete aggregated observationY_(A) ^((AggObs)) value if: a) the observation/climatology qualityfactor Q_(A) exceeds a user-defined threshold Q^(thresh) and b) thedistance between A and B d_(A,B) is less than a quality-weightedcorrelation distance. For locations impacted by multiple observations, adecision for replacing the discrete unified model value Y_(B)^((UniModel)) with any of the n qualifying neighboring observations canbe made by making a discrete weighted average of the n contributions.The revised uncertainty estimate is either the original estimate for theaggregated observation Y_(A) ^((AggObs)) or a modified versionreflecting the additional processing to achieve the virtual observationat location B.

Additional consideration may be required for the cases when anobservation is a proxy for a model variable but has substantiallydifferent characteristics. For example, vehicle traction systems employsensors that provide data that can be considered observations oftraction or friction. In some cases, it is desirable to relate eithertraction or friction to road weather conditions. For example, a very lowtraction value may be used to infer that a road is icy. Likewise, wipersin an “on” state may be a proxy for precipitation even though an “on”state can also mean the wipers are being used during road splash orwashing fluid situations when no precipitation is occurring. Whilesignificant ambiguity exists in relating such proxy observations tomodel variables, it is important to include the process.

These proxy observation cases are readily addressed using the describedmethodology. In such cases, the quality factor Q_(A) can be adjusted toreflect any additional uncertainty introduced by the ambiguous mappingof the observation to the related model variable. For example, imaginewe are reasonably confident that a traction measurement T^((AggObs))with range 0-1 corresponds to an icy road with probability 1 whenT^((AggObs))=0 and probability 0 when T^((AggObs))=0.3. The factor H_(A)in the equation for Q_(A) might be then given, for example, by:

$\begin{matrix}{H_{A} = {1 - {T_{A}^{({AggObs})}/0.3}}} & {{{for}T_{A}^{({AggObs})}}<=0.3} \\0 & {T_{A}^{({AggObs})} > 0.3}\end{matrix}$

The particular formula for HA is likely to be empirical and situational,based on the nature of the sensor and how its data is transformed todetermine the model equivalent. In this example, an actual observationof traction is used to generate a virtual observation of road condition.Similarly, an actual observation of wiper motor speed could be used togenerate a virtual observation of precipitation rate. The inverseprocess can be used to relate model variables to their equivalents asmeasured by the mobile sensor. The particular formula that works bestfor any pair of model variables and proxy mobile observation, and forany parameters contained within that formula, can be refined andimproved by analyses (both real-time and offline) that statisticallycompare forecasted values of the variable to observations.

Virtual observations can be used in a manner similar to stationaryobservations as input to NWP models and to post-processing. For example,for NWP model bias adjustment, the virtual observation history is inputto the system incrementally (as virtual observations come available), inthe same manner as data from a stationary observation. If the system canaccommodate uncertainty values related to each virtual observation, thatinformation is available. This step traditionally may employ a ModelOutput Statistics (MOS) or Dynamic Model Output Statistics (DMOS)process to bias correct each constituent NWP model. To do that, a timeseries (typically 30-90 days for DMOS) reflecting the model error(defined as Yn(OrigModel)−Yn(Virtual)) is accumulated and statisticallyevaluated to determine a regression fit relating model values tocorresponding observations. That regression fit is used to determine amodel adjustment Yn(ModelAdj), which is most commonly a simple bias butmay be a more sophisticated model correction such as atemperature-dependent bias. Following the standard methodology, butusing the regression based on virtual observations, for each NWP model:

Y _(n) ^((UnbiasedModel)) =Y _(n) ^((OrigModel)) −Y _(n) ^((ModelAdj))

The quality of the regression fit can be determined from the regressiondata, and quality control can be applied as with stationaryobservations.

For consensus NWP combination, the virtual observation history is inputto the system incrementally (as virtual observations come available), inthe same manner as data from a stationary observation. The observationhistory is then used to compute model weights for each location. If thesystem can accommodate uncertainty values related to each aggregated orvirtual observation, that information is available.

Error correction for current conditions and shorter-term forecasts(typically the first few hours) is performed using each virtualobservation in a manner similar to a stationary observation. Theextrapolation technique for virtual observation generation is used tomake corrections at locations lacking current observations. If thesystem can accommodate uncertainty values related to each virtualobservation, that information is available. This includes using thevirtual observation to modify an estimate of confidence computed as partof the forecast, and using the virtual observation to flag “outlier”events for which a “blown” forecast may be substantially different fromthe virtual observation.

For extrapolation to non-sensor locations, with a sufficient density ofmobile observations, the methodology above replaces other methods forextrapolating sensor observations to non-sensor locations.

It should be clear that a “spin up” period may be required to accumulatethe needed climatology and time history before reliable virtualobservations can be generated and used in post-processing. Experiencewith traditional post-processing indicates that this period is 30-90days, depending on the fidelity desired.

Because virtual observations generalize the notion of an observation,the methodology works for stationary observations as well as mobileobservations. For example, previously it was not possible to effectivelyextrapolate, with any fine-scale fidelity, fixed or stationaryobservations (such as from airport weather stations or RWIS) tosurrounding locations that themselves lack observations. No quantitativeguidance was available to identify how far from the observation locationthe knowledge of the observation could be applied to improve weatherinformation. For example, it is expected conceptually that anobservation at an airport in a flat geography is reflective of weatherin an area around the airport that is considerably larger than for anairport in a mountainous area. The climatology and related correlationdistance information developed using mobile observations, combined withgeographic coverage around a stationary observation, can be used toaccurately extrapolate the stationary observations within thecorrelation distance of the stationary observation—any location, notjust along roads.

Virtual observations are robust to a lack of observational data. Twosituations may occur when observations are lacking at a particularlocation. At locations where insufficient mobile (or stationary)observations are available to compute a reliable climatology ordetermine a correlation distance, the virtual observation will have theform of the original unified model value at that location, adjusted byany observations that are within the correlation distance. At locationswith the additional characteristic of not being within a correlationlength of an observation over the entire climatology period or in thereal-time data, the virtual observation will be equal to the unifiedmodel value. With a sufficiently dense mobile observation source, suchsituations are expected to be rare.

By the nature of the extrapolation methods used, information will beconsistent across both observation-rich locations and observation-sparselocations. In other words, locations neighboring those with noobservations whatsoever will themselves have only small adjustments,ensuring spatial continuity.

FIG. 3 shows a method 300 for using mobile data to improve weatherinformation. As shown in FIG. 3 , the method 300 receives stationaryobservation data provided by a stationary weather station in step 310and receives data from a plurality of input weather models 320. In step330, the method 300 generates unified weather model estimates based onthe stationary observation data and the input weather model data. Themethod 300, in step 340, aggregates mobile observation data provided bya plurality of non-stationary sensors. The method 300 uses theaggregated mobile observation data to adjust the unified weather modelestimates in step 350. The method 300 may be performed by the system 100described above, in particular, the weather prediction station 120and/or the processor 130, although any suitable system or systemcomponents may be employed.

The method 300 may further determine virtual observation data based onthe adjustment of the weather model estimates, the virtual observationdata being an estimate of observation data that would have been providedby a non-existent stationary weather station. For example, virtualobservations Yn(Virtual) may be calculated using an observationadjustment Yn(ObsAdj) of a unified model estimate Yn(UniModel) atlocation n.

The method 300 may also determine virtual observation data based on theadjustment of the weather model estimates by determining climatologicalvalues based on the aggregated mobile observation data and using theclimatological values to determine the virtual observation data. Forexample, as is described above, an aggregated mobile observation of avariable in the aggregated mobile observation data for every hour over aone-month period could be averaged over that month to determine the meanvalue of the variable during that month at each location.

The method may also use the virtual observations as input to NWP modelsand to perform post-processing on a weather prediction model. Forexample, as described above, the virtual observation Yn(Virtual) atlocation n may be used in post-processing for an NWP model estimate forlocation n. In one particular example, a time series reflecting themodel error (defined as Yn(OrigModel)−Yn(Virtual)) may be accumulatedand statistically evaluated to determine a regression fit relating modelvalues to corresponding observations. That regression fit is used todetermine a model adjustment Yn(ModelAdj), which can be used for biascorrection.

The step 350 of using the aggregated mobile observation data to adjustthe weather model estimates may comprise spatially correlating themobile observation data to a virtual observation location and adjustingthe weather model estimates at the virtual observation location. Forexample, as described above, the aggregated observation data may be usedto generate an observation adjustment Yn(ObsAdj) for a location n frommobile observation data at location A. The observation adjustmentYn(ObsAdj) may be used to adjust the unified model estimate Yn(UniModel)for the location n. This adjustment may determine the virtualobservation Yn(Virtual) at location n.

Spatially correlating the mobile observation data to a virtualobservation location may include determining a spatial correlationdistance, such as the spatial correlation distance dbinCORR describedabove with reference to FIG. 2 , defining a distance over which anobservation may be usefully extrapolated to adjust the unified weathermodel estimates. The spatial correlation distance may have a directionand magnitude component. Accordingly, a plurality of spatial correlationdistances may define a spatial correlation region of varying shapes andsizes, such as the spatial correlation region 220 shown in FIG. 2 .

The mobile observation data provided by the plurality of non-stationarysensors may be of a spatiotemporal bin defined by location and timeparameters previously occupied by the plurality of non-stationarysensors. For example, with reference to FIG. 2 , the mobile observationdata provided by the plurality of non-stationary sensors 140 may be of aspatiotemporal bin 210 that each non-stationary sensor 140 passesthrough over the course of the temporal parameter (e.g., 1 hour) of thespatiotemporal bin 210.

The method 300 may be implemented by software similar to what is used inNWP modeling and in standard post-processing. Accordingly, the system100, such as the weather prediction station 120 and/or the processor130, may be configured to perform the steps of the method 300, orsimilar method. As a result, the system 100 and method 300 are designedto accommodate observations of varying quality, and with unknown spatialand temporal density. The system 100 and method 300 are thus applicableto a wide range of mobile observation scenarios and may be implementedat arbitrary space-time resolution, depending on the characteristics ofthe available observations.

The system 100 and method 300 provides at least three improvements overuse of model data without access to mobile observations. First, themodel bias adjustment, made possible at all locations by the mobileobservation climatology, enables fine spatial-scale improvements tomodel conditions/forecasts. Second, model weights for consensuscombinations can be computed by location rather than assumed constantover large regions. Third, when the observations are available inreal-time, they can be used to reduce model errors in real-time forcurrent conditions and near-time forecasts. The system 100 and method300 makes it possible to extend those improvements, through use of thecorrelation distance, to locations where no observations are available.

The detailed descriptions of the above embodiments are not exhaustivedescriptions of all embodiments contemplated by the inventors to bewithin the scope of the present description. Indeed, persons skilled inthe art will recognize that certain elements of the above-describedembodiments may variously be combined or eliminated to create furtherembodiments, and such further embodiments fall within the scope andteachings of the present description. It will also be apparent to thoseof ordinary skill in the art that the above-described embodiments may becombined in whole or in part to create additional embodiments within thescope and teachings of the present description.

Thus, although specific embodiments are described herein forillustrative purposes, various equivalent modifications are possiblewithin the scope of the present description, as those skilled in therelevant art will recognize. The teachings provided herein can beapplied to other systems and methods for using weather data to improveweather information and not just to the embodiments described above andshown in the accompanying figures. Accordingly, the scope of theembodiments described above should be determined from the followingclaims.

1. (canceled)
 2. A method comprising: receiving a unified weather modeloutput; receiving a statistical correlation that relates weatherobservation data for a spatial bin to a virtual observation locationbased on a distance; receiving aggregate mobile observation dataprovided by a plurality of non-stationary sensors for the spatial bin;and adjusting the unified weather model output based on the statisticalcorrelation and the aggregate mobile observation data.
 3. The method ofclaim 2, wherein the weather observation data for the spatial binincludes the aggregate mobile observation data.
 4. The method of claim2, wherein the statistical correlation that relates the weatherobservation data for the spatial bin and the virtual observationlocation is further based on a time of day.
 5. The method of claim 2,wherein the statistical correlation that relates the weather observationdata for the spatial bin and the virtual observation location is furtherbased on an annual time period.
 6. The method of claim 2, wherein theunified weather model output is generated using an input weather modelutilizing stationary observation data as input.
 7. The method of claim2, wherein the statistical correlation is calculated from the distancebetween the spatial bin and the virtual observation location divided bya correlation distance for the spatial bin.
 8. The method of claim 7,wherein the statistical correlation is further calculated using asensitivity to the aggregate mobile observation data.
 9. The method ofclaim 2, wherein the unified weather model output is at least an hourold.
 10. A system comprising: a memory; and a processor coupled to thememory, the processor being configured to: receive a unified weathermodel output; receive a statistical correlation that relates weatherobservation data for a spatial bin to a virtual observation locationbased on a distance; receive aggregate mobile observation data providedby a plurality of non-stationary sensors for the spatial bin; and adjustthe unified weather model output based on the statistical correlationand the aggregate mobile observation data.
 11. The system of claim 10,wherein the weather observation data for the spatial bin includes theaggregate mobile observation data.
 12. The system of claim 10, whereinthe statistical correlation that relates the weather observation datafor the spatial bin and the virtual observation location is furtherbased on a time of day.
 13. The system of claim 10, wherein thestatistical correlation that relates the weather observation data forthe spatial bin and the virtual observation location is further based onan annual time period.
 14. The system of claim 10, wherein the unifiedweather model output is generated using an input weather model usingstationary observation data as input.
 15. The system of claim 10,wherein the statistical correlation is calculated from the distancebetween the spatial bin and the virtual observation location divided bya correlation distance for the spatial bin.
 16. The system of claim 15,wherein the statistical correlation is further calculated using asensitivity to the aggregate mobile observation data.
 17. The system ofclaim 10, wherein the unified weather model output is at least an hourold.